
import Head from 'next/head'

<Head>
  <script>
    {
      `(function() {
         var _hmt = _hmt || [];
(function() {
  var hm = document.createElement("script");
  hm.src = "https://hm.baidu.com/hm.js?e60fb290e204e04c5cb6f79b0ac1e697";
  var s = document.getElementsByTagName("script")[0]; 
  s.parentNode.insertBefore(hm, s);
})();
       })();`
    }
  </script>
</Head>

![LangChain](https://pica.zhimg.com/50/v2-56e8bbb52aa271012541c1fe1ceb11a2_r.gif)





基于向量存储的记忆[#](#vectorstore-backed-memory "本标题的永久链接")
===================================================

`VectorStoreRetrieverMemory`将记忆存储在VectorDB中，并在每次调用时查询最重要的K个文档。

与大多数其他记忆类不同的是，它不明确跟踪交互的顺序。

在这种情况下，“文档”是先前的对话片段。这可以用来提到AI在对话中早期被告知的相关信息。

```python
from datetime import datetime
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.llms import OpenAI
from langchain.memory import VectorStoreRetrieverMemory
from langchain.chains import ConversationChain
from langchain.prompts import PromptTemplate

```

初始化您的VectorStore[#](#initialize-your-vectorstore "本标题的永久链接")
------------------------------------------------------------

根据您选择的存储方式，此步骤可能会有所不同。有关更多详细信息，请参阅相关的VectorStore文档。

```python
import faiss

from langchain.docstore import InMemoryDocstore
from langchain.vectorstores import FAISS

embedding_size = 1536 # Dimensions of the OpenAIEmbeddings
index = faiss.IndexFlatL2(embedding_size)
embedding_fn = OpenAIEmbeddings().embed_query
vectorstore = FAISS(embedding_fn, index, InMemoryDocstore({}), {})

```

创建您的VectorStoreRetrieverMemory[#](#create-your-the-vectorstoreretrievermemory "本标题的永久链接")
-----------------------------------------------------------------------------------------

记忆体对象是从任何VectorStoreRetriever实例化的。

```python
# In actual usage, you would set `k` to be a higher value, but we use k=1 to show that
# the vector lookup still returns the semantically relevant information
retriever = vectorstore.as_retriever(search_kwargs=dict(k=1))
memory = VectorStoreRetrieverMemory(retriever=retriever)

# When added to an agent, the memory object can save pertinent information from conversations or used tools
memory.save_context({"input": "My favorite food is pizza"}, {"output": "thats good to know"})
memory.save_context({"input": "My favorite sport is soccer"}, {"output": "..."})
memory.save_context({"input": "I don't the Celtics"}, {"output": "ok"}) # 

```

```python
# Notice the first result returned is the memory pertaining to tax help, which the language model deems more semantically relevant
# to a 1099 than the other documents, despite them both containing numbers.
print(memory.load_memory_variables({"prompt": "what sport should i watch?"})["history"])

```

```python
input: My favorite sport is soccer
output: ...

```

在链中使用[#](#using-in-a-chain "本标题的永久链接")
--------------------------------------

让我们通过一个例子来演示，再次设置`verbose=True`以便我们可以看到提示。

```python
llm = OpenAI(temperature=0) # Can be any valid LLM
_DEFAULT_TEMPLATE = """The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

Relevant pieces of previous conversation:
{history}

(You do not need to use these pieces of information if not relevant)

Current conversation:
Human: {input}
AI:"""
PROMPT = PromptTemplate(
    input_variables=["history", "input"], template=_DEFAULT_TEMPLATE
)
conversation_with_summary = ConversationChain(
    llm=llm, 
    prompt=PROMPT,
    # We set a very low max_token_limit for the purposes of testing.
    memory=memory,
    verbose=True
)
conversation_with_summary.predict(input="Hi, my name is Perry, what's up?")

```

```python
> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

Relevant pieces of previous conversation:
input: My favorite food is pizza
output: thats good to know

(You do not need to use these pieces of information if not relevant)

Current conversation:
Human: Hi, my name is Perry, what's up?
AI:

> Finished chain.

```

```python
" Hi Perry, I'm doing well. How about you?"

```

```python
# Here, the basketball related content is surfaced
conversation_with_summary.predict(input="what's my favorite sport?")

```

```python
> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

Relevant pieces of previous conversation:
input: My favorite sport is soccer
output: ...

(You do not need to use these pieces of information if not relevant)

Current conversation:
Human: what's my favorite sport?
AI:

> Finished chain.

```

```python
' You told me earlier that your favorite sport is soccer.'

```

```python
# Even though the language model is stateless, since relavent memory is fetched, it can "reason" about the time.
# Timestamping memories and data is useful in general to let the agent determine temporal relevance
conversation_with_summary.predict(input="Whats my favorite food")

```

```python
> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

Relevant pieces of previous conversation:
input: My favorite food is pizza
output: thats good to know

(You do not need to use these pieces of information if not relevant)

Current conversation:
Human: Whats my favorite food
AI:

> Finished chain.

```

```python
' You said your favorite food is pizza.'

```

```python
# The memories from the conversation are automatically stored,
# since this query best matches the introduction chat above,
# the agent is able to 'remember' the user's name.
conversation_with_summary.predict(input="What's my name?")

```

```python
> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

Relevant pieces of previous conversation:
input: Hi, my name is Perry, what's up?
response: Hi Perry, I'm doing well. How about you?

(You do not need to use these pieces of information if not relevant)

Current conversation:
Human: What's my name?
AI:

> Finished chain.

```

```python
' Your name is Perry.'

```

